Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30099 discussions

Parenchyma over CSF ratio
by Mahmoud 22 Oct '14

22 Oct '14

Dear all, I used the the 3 steps of longitudinal analysis explained in freesurfer longitudinal tutorial and need to compute the ratio Parenchyma/CSF for each of time points. Is this equivalent to compute the (total gray matter volume + total cortical matter volume)/CSF from aseg.stats file ? Thank you, Mahmoud

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Aparc stats with different input
by Makaretz, Sara Johanna 22 Oct '14

22 Oct '14

Hi, I have a PET volume that is registered to a recon, then projected to the surface via vol2surf. I would like to pull stats for the PET using aparc regions. Should I use mri_annotation2label, then pull stats for each label? Or is there something similar to mri_segstats, ie. --annot lh.aparc.annot --in pet_surf.mgz --ctab-default --sum stats.table ? Thank you for your help! Sara

2 1

rcbf-prep outputting negative values
by Asif Jamil 22 Oct '14

22 Oct '14

Hello, I have pcASL data from a Siemens 3T scanner which I would like to process using the provided rcbf-prep command. The process completes smoothly without error. However when I inspect my roi.dat file, I observe that all the ROI values are actually negative, and seem to be beyond physiological CBF range (~60-120 ml/100g/min). The roi.dat file is pasted below. Would there be any reason to suspect that the tag-control subtraction might be running inverted? I was trying to find a way to customize the command, but seems like there’s no option to specifying the order of tag and control. Any help would be appreciated! Best, Asif # Title Segmentation Statistics # # generating_program mri_segstats # cvs_version $Id: mri_segstats.c,v 1.101 2013/08/20 15:52:54 greve Exp $ # cmdline mri_segstats --seg /www/data/fMRI/subjects/PV/mri/aparc+aseg.mgz --ctab /www/data/fMRI/ANODAL/PV_2.0_hum_10387/ASL/30/rcbf/roi.table --i /www/data/fMRI/ANODAL/PV_2.0_hum_10387/ASL/30/rcbf/rcbf.anat.nii --sum /www/data/fMRI/ANODAL/PV_2.0_hum_10387/ASL/30/rcbf/roi.dat # sysname Darwin # hostname MBP-4.local # machine x86_64 # user asif # anatomy_type volume # # SegVolFile /www/data/fMRI/subjects/PV/mri/aparc+aseg.mgz # SegVolFileTimeStamp 2014/10/21 08:43:42 # ColorTable /www/data/fMRI/ANODAL/PV_2.0_hum_10387/ASL/30/rcbf/roi.table # ColorTableTimeStamp 2014/10/22 15:52:27 # InVolFile /www/data/fMRI/ANODAL/PV_2.0_hum_10387/ASL/30/rcbf/rcbf.anat.nii # InVolFileTimeStamp 2014/10/22 16:00:34 # InVolFrame 0 # Only reporting non-empty segmentations # VoxelVolume_mm3 1 # TableCol 1 ColHeader Index # TableCol 1 FieldName Index # TableCol 1 Units NA # TableCol 2 ColHeader SegId # TableCol 2 FieldName Segmentation Id # TableCol 2 Units NA # TableCol 3 ColHeader NVoxels # TableCol 3 FieldName Number of Voxels # TableCol 3 Units unitless # TableCol 4 ColHeader Volume_mm3 # TableCol 4 FieldName Volume # TableCol 4 Units mm^3 # TableCol 5 ColHeader StructName # TableCol 5 FieldName Structure Name # TableCol 5 Units NA # TableCol 6 ColHeader Mean # TableCol 6 FieldName Intensity Mean # TableCol 6 Units unknown # TableCol 7 ColHeader StdDev # TableCol 7 FieldName Itensity StdDev # TableCol 7 Units unknown # TableCol 8 ColHeader Min # TableCol 8 FieldName Intensity Min # TableCol 8 Units unknown # TableCol 9 ColHeader Max # TableCol 9 FieldName Intensity Max # TableCol 9 Units unknown # TableCol 10 ColHeader Range # TableCol 10 FieldName Intensity Range # TableCol 10 Units unknown # NRows 114 # NTableCols 10 # ColHeaders Index SegId NVoxels Volume_mm3 StructName Mean StdDev Min Max Range 1 0 15692031 15692031.0 Unknown -74.0533 149.7173 -409.6000 212.1609 621.7609 2 2 223002 223002.0 Left-Cerebral-White-Matter -225.7412 41.4942 -378.4684 43.0771 421.5454 3 4 5142 5142.0 Left-Lateral-Ventricle -213.5301 25.6550 -268.4635 -140.5092 127.9543 4 5 210 210.0 Left-Inf-Lat-Vent -250.1857 11.2349 -268.3093 -227.5028 40.8065 5 7 12201 12201.0 Left-Cerebellum-White-Matter -184.3329 132.5210 -314.1348 0.0000 314.1348 6 8 38822 38822.0 Left-Cerebellum-Cortex -106.5107 126.4406 -397.5404 0.0000 397.5404 7 10 7802 7802.0 Left-Thalamus-Proper -256.3931 10.4379 -280.3882 -205.4267 74.9615 8 11 4000 4000.0 Left-Caudate -202.6371 20.4855 -256.5304 -151.9264 104.6040 9 12 6375 6375.0 Left-Putamen -225.6037 14.5390 -274.2597 -175.4577 98.8020 10 13 1502 1502.0 Left-Pallidum -253.1898 12.3024 -277.3420 -185.7775 91.5645 11 14 550 550.0 3rd-Ventricle -225.0951 31.5825 -306.7295 -134.6138 172.1157 12 15 964 964.0 4th-Ventricle -212.5151 112.4167 -288.3225 0.0000 288.3225 13 16 18186 18186.0 Brain-Stem -234.2281 112.8928 -319.7348 0.0000 319.7348 14 17 4114 4114.0 Left-Hippocampus -261.7396 17.4063 -339.8024 -223.4028 116.3996 15 18 1513 1513.0 Left-Amygdala -253.4237 8.8525 -288.2225 -232.8777 55.3447 16 24 847 847.0 CSF -257.5531 13.5256 -300.9257 -212.2279 88.6977 17 26 535 535.0 Left-Accumbens-area -227.1246 45.3633 -360.7124 -138.9498 221.7626 18 28 3786 3786.0 Left-VentralDC -270.4991 24.1379 -308.4358 -148.3388 160.0970 19 30 35 35.0 Left-vessel -239.3001 10.2992 -259.7188 -227.2777 32.4411 20 31 1442 1442.0 Left-choroid-plexus -230.4919 15.5479 -280.8550 -169.9589 110.8961 21 41 223786 223786.0 Right-Cerebral-White-Matter -226.0802 45.5920 -388.7630 99.2566 488.0197 22 43 4499 4499.0 Right-Lateral-Ventricle -218.3645 29.3800 -283.5730 -136.7349 146.8381 23 44 124 124.0 Right-Inf-Lat-Vent -224.9743 6.5724 -238.9407 -213.7236 25.2171 24 46 12584 12584.0 Right-Cerebellum-White-Matter -193.3710 131.2612 -321.5745 0.0000 321.5745 25 47 39998 39998.0 Right-Cerebellum-Cortex -115.9971 131.6163 -397.0068 0.0000 397.0068 26 49 7671 7671.0 Right-Thalamus-Proper -257.6761 12.1048 -286.1383 -201.9509 84.1875 27 50 3771 3771.0 Right-Caudate -194.8961 24.1022 -261.2213 -130.2366 130.9848 28 51 5541 5541.0 Right-Putamen -207.9626 20.2756 -263.4107 -152.3473 111.0634 29 52 1586 1586.0 Right-Pallidum -240.4646 16.8542 -274.6204 -178.9617 95.6588 30 53 4340 4340.0 Right-Hippocampus -255.1402 15.5864 -317.2354 -214.7629 102.4725 31 54 1443 1443.0 Right-Amygdala -223.3856 13.5761 -275.9869 -188.8713 87.1156 32 58 599 599.0 Right-Accumbens-area -233.9969 41.2913 -328.8579 -130.0125 198.8454 33 60 3578 3578.0 Right-VentralDC -265.3871 29.2565 -302.4017 -137.6554 164.7464 34 62 42 42.0 Right-vessel -204.7102 4.3293 -215.7804 -196.4114 19.3690 35 63 1778 1778.0 Right-choroid-plexus -234.2253 23.0682 -286.4693 -173.1205 113.3488 36 72 5 5.0 5th-Ventricle -224.2637 0.6994 -224.9757 -223.0969 1.8788 37 77 1198 1198.0 WM-hypointensities -237.1980 23.6141 -288.2162 -124.3671 163.8491 38 80 6 6.0 non-WM-hypointensities -219.0157 25.7339 -241.4550 -178.7211 62.7339 39 85 240 240.0 Optic-Chiasm -339.9201 33.8276 -387.3214 -244.3237 142.9977 40 251 1125 1125.0 CC_Posterior -287.1999 13.0189 -328.2475 -253.4940 74.7535 41 252 474 474.0 CC_Mid_Posterior -258.9252 7.4909 -275.8824 -243.3831 32.4993 42 253 466 466.0 CC_Central -243.8036 9.1146 -260.0744 -213.7926 46.2818 43 254 506 506.0 CC_Mid_Anterior -220.1470 10.0537 -248.5798 -188.5451 60.0347 44 255 913 913.0 CC_Anterior -214.1350 20.9075 -243.5242 -150.2715 93.2527 45 1000 723 723.0 ctx-lh-unknown -268.9304 44.9523 -368.2704 -151.4314 216.8390 46 1001 2368 2368.0 ctx-lh-bankssts -181.3014 22.6124 -237.0966 -91.1488 145.9478 47 1002 2124 2124.0 ctx-lh-caudalanteriorcingulate -166.5679 24.2600 -239.7585 -107.8335 131.9250 48 1003 5407 5407.0 ctx-lh-caudalmiddlefrontal -138.6242 38.4188 -223.4834 -4.1240 219.3594 49 1005 2805 2805.0 ctx-lh-cuneus -219.2092 24.4863 -307.2400 -100.9523 206.2876 50 1006 1835 1835.0 ctx-lh-entorhinal -313.5578 37.2401 -400.2783 -245.7666 154.5116 51 1007 9297 9297.0 ctx-lh-fusiform -271.4266 44.9975 -403.3750 -178.3286 225.0465 52 1008 11208 11208.0 ctx-lh-inferiorparietal -198.1575 29.6503 -337.2401 -97.1248 240.1152 53 1009 9229 9229.0 ctx-lh-inferiortemporal -292.8779 58.9245 -402.0195 -139.4616 262.5579 54 1010 2200 2200.0 ctx-lh-isthmuscingulate -256.2006 13.6217 -309.5894 -221.2736 88.3158 55 1011 10741 10741.0 ctx-lh-lateraloccipital -241.9617 44.5052 -387.3303 -134.7086 252.6217 56 1012 6759 6759.0 ctx-lh-lateralorbitofrontal -234.8751 55.8340 -391.7819 -116.4604 275.3214 57 1013 5512 5512.0 ctx-lh-lingual -252.9017 15.0886 -364.8814 -199.6257 165.2558 58 1014 3917 3917.0 ctx-lh-medialorbitofrontal -224.2145 76.9983 -385.2454 -15.3043 369.9411 59 1015 10136 10136.0 ctx-lh-middletemporal -215.7101 41.6577 -379.8396 -89.4147 290.4249 60 1016 2054 2054.0 ctx-lh-parahippocampal -288.6394 39.2564 -387.4811 -235.1773 152.3038 61 1017 3422 3422.0 ctx-lh-paracentral -233.0926 13.5023 -263.2673 -170.3670 92.9003 62 1018 4574 4574.0 ctx-lh-parsopercularis -149.2349 39.6032 -217.8855 9.6249 227.5103 63 1019 1593 1593.0 ctx-lh-parsorbitalis -169.5554 35.9393 -282.0561 -72.7232 209.3329 64 1020 3291 3291.0 ctx-lh-parstriangularis -144.1005 36.0805 -230.1887 4.5093 234.6980 65 1021 1713 1713.0 ctx-lh-pericalcarine -231.6538 8.5455 -255.3997 -203.6041 51.7957 66 1022 10227 10227.0 ctx-lh-postcentral -181.9029 37.4439 -299.1582 -21.3713 277.7869 67 1023 2807 2807.0 ctx-lh-posteriorcingulate -230.5880 18.4718 -274.6758 -173.2297 101.4461 68 1024 12919 12919.0 ctx-lh-precentral -183.6406 39.6604 -306.2378 -40.3290 265.9088 69 1025 9442 9442.0 ctx-lh-precuneus -230.5928 15.9918 -306.5322 -163.3685 143.1636 70 1026 2548 2548.0 ctx-lh-rostralanteriorcingulate -157.6567 39.7513 -254.3800 -89.2986 165.0814 71 1027 13071 13071.0 ctx-lh-rostralmiddlefrontal -83.8164 67.7580 -394.8940 134.8738 529.7678 72 1028 21736 21736.0 ctx-lh-superiorfrontal -122.8224 69.0962 -316.2417 196.0131 512.2548 73 1029 12628 12628.0 ctx-lh-superiorparietal -221.9534 26.6104 -364.8822 -107.6960 257.1862 74 1030 11394 11394.0 ctx-lh-superiortemporal -186.1106 40.3886 -350.9529 -40.3714 310.5815 75 1031 10576 10576.0 ctx-lh-supramarginal -176.2239 42.3754 -354.6027 -41.8400 312.7627 76 1032 595 595.0 ctx-lh-frontalpole -283.6195 82.6117 -394.6566 -37.1957 357.4609 77 1033 2431 2431.0 ctx-lh-temporalpole -277.4131 30.0602 -374.9873 -178.5805 196.4068 78 1034 1232 1232.0 ctx-lh-transversetemporal -210.0555 15.6700 -241.2193 -159.1772 82.0421 79 1035 6246 6246.0 ctx-lh-insula -206.2323 18.6802 -262.2652 -161.5516 100.7136 80 2000 486 486.0 ctx-rh-unknown -235.6483 29.0666 -303.1176 -176.8145 126.3031 81 2001 1910 1910.0 ctx-rh-bankssts -165.2222 33.7818 -232.1002 -46.5102 185.5900 82 2002 1806 1806.0 ctx-rh-caudalanteriorcingulate -173.3855 25.8466 -243.1284 -116.1957 126.9327 83 2003 5075 5075.0 ctx-rh-caudalmiddlefrontal -164.3494 26.3759 -240.7866 -75.2611 165.5255 84 2005 3408 3408.0 ctx-rh-cuneus -241.5760 20.8712 -370.4695 -182.1323 188.3371 85 2006 1327 1327.0 ctx-rh-entorhinal -291.8428 46.4631 -397.5590 -204.5806 192.9784 86 2007 8621 8621.0 ctx-rh-fusiform -277.8799 38.7263 -398.6075 -180.0617 218.5457 87 2008 12648 12648.0 ctx-rh-inferiorparietal -203.4482 32.5607 -354.4304 -108.6639 245.7665 88 2009 9708 9708.0 ctx-rh-inferiortemporal -264.8841 66.1289 -400.9079 -77.6510 323.2569 89 2010 2341 2341.0 ctx-rh-isthmuscingulate -265.9744 15.0482 -310.5466 -229.7068 80.8398 90 2011 11259 11259.0 ctx-rh-lateraloccipital -249.5812 43.6094 -392.7505 -137.7381 255.0125 91 2012 6527 6527.0 ctx-rh-lateralorbitofrontal -205.4345 57.0364 -379.2646 -52.3057 326.9589 92 2013 6336 6336.0 ctx-rh-lingual -270.3578 14.8451 -375.5747 -218.0085 157.5662 93 2014 4191 4191.0 ctx-rh-medialorbitofrontal -217.2649 76.5895 -381.2976 -28.7736 352.5240 94 2015 12028 12028.0 ctx-rh-middletemporal -166.6064 53.4122 -370.0519 42.7304 412.7823 95 2016 1888 1888.0 ctx-rh-parahippocampal -291.0411 34.0657 -386.3947 -243.3085 143.0863 96 2017 3708 3708.0 ctx-rh-paracentral -239.3826 13.8495 -269.6346 -180.5456 89.0890 97 2018 4159 4159.0 ctx-rh-parsopercularis -124.4858 40.3498 -206.4271 34.1722 240.5993 98 2019 2191 2191.0 ctx-rh-parsorbitalis -127.3451 51.8375 -295.4905 11.9257 307.4162 99 2020 3475 3475.0 ctx-rh-parstriangularis -84.7648 53.4594 -190.6588 101.4333 292.0922 100 2021 2007 2007.0 ctx-rh-pericalcarine -250.7360 12.8022 -325.5542 -173.3052 152.2490 101 2022 9599 9599.0 ctx-rh-postcentral -200.0130 39.5983 -299.2733 -53.0320 246.2413 102 2023 2682 2682.0 ctx-rh-posteriorcingulate -235.8722 18.9181 -285.3879 -169.6746 115.7133 103 2024 13064 13064.0 ctx-rh-precentral -187.4779 45.2968 -282.6149 -29.0527 253.5621 104 2025 9932 9932.0 ctx-rh-precuneus -240.3425 16.8327 -338.1844 -166.0625 172.1219 105 2026 1904 1904.0 ctx-rh-rostralanteriorcingulate -159.2295 47.7504 -328.0970 -69.7684 258.3285 106 2027 13009 13009.0 ctx-rh-rostralmiddlefrontal -76.5586 64.9635 -388.0326 139.6366 527.6691 107 2028 19644 19644.0 ctx-rh-superiorfrontal -136.2872 62.1116 -250.7094 117.8829 368.5923 108 2029 12094 12094.0 ctx-rh-superiorparietal -231.3411 27.9200 -377.6333 -148.5919 229.0414 109 2030 11110 11110.0 ctx-rh-superiortemporal -144.3232 45.9093 -303.4510 -4.4534 298.9976 110 2031 11218 11218.0 ctx-rh-supramarginal -183.9012 36.5165 -339.1638 -46.8222 292.3416 111 2032 735 735.0 ctx-rh-frontalpole -225.4727 127.2478 -388.1892 80.9771 469.1663 112 2033 1999 1999.0 ctx-rh-temporalpole -237.0710 32.1687 -352.4148 -168.1622 184.2525 113 2034 948 948.0 ctx-rh-transversetemporal -197.8872 24.2673 -238.0692 -137.0336 101.0356 114 2035 6087 6087.0 ctx-rh-insula -179.7504 28.2217 -253.5477 -103.3342 150.2135

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Running mri_glmfit-sim with pre-computed Monte-Carlo
by Thomas DeRamus 21 Oct '14

21 Oct '14

Dear Freesurfer Experts, I have a label file of a number of ROI's I've made based on a mask. I'm trying to get an idea of what cluster are significant within a focused region of the label file. To make the correction a little less sever, I pre-computed some monte-carlo values using mri_mcsim mri_mcsim --o /media/rkbatch/gyrification/fsaverage/mult-comp-cor/fsaverage/rh/Socialbrain --base mc-z --surface fsaverage rh --nreps 10000 --label /media/rkbatch/gyrification/fsaverage/label/rh.SocialBrainMask mri_mcsim --o $SUBJECTS_DIR/fsaverage/mult-comp-cor/fsaverage/lh/Socialbrain --base mc-z --surface fsaverage lh --nreps 10000 --label $SUBJECTS_DIR/fsaverage/label/lh.SocialBrainMask Respectively for each surface (the labels are different on each hemisphere). I've run mri_glmfit and have the uncorrected outputs and results (there are actually 6 of these, one for volume, thickness, and surface area for each hemi): mri_glmfit --y rh.diagnosisxagexrightThickslopetest.thickness.10.mgh --fsgd SBM_glmRightthickNOGENDER.fsgd dods --C diagnosisnonlGIICmeas.mtx --C agesxdiagnosisnonlGI.mtx --C diagnosisnonxagexICmeas.mtx --C ICmeasurexdiagnosisnonlGI.mtx --surf fsaverage rh --label /media/rkbatch/gyrification/fsaverage/label/rh.SocialBrainMask --glmdir rh.ALESocial__agethickness.glmdir But when I try to do the mri_glmfit-sim, I am having trouble getting it to read in the corrected csd file. I'm wanting to run it at cwp of 0.05 at a threshold of 2 (0.01), with a correction for two hemispheres. However, when I try to run it with --cache-dir /media/rkbatch/gyrification/fsaverage/mult-comp-cor/fsaverage/lh/Socialbrain , it has trouble understanding the --cwp .05, --2spaces and 2 abs (after cache dir) flags. Additionally, it wants a --sim and --sim-sign value like it wants to compute a monte-carlo from scratch. Could I get an example on how best to direct it to the simulation I've run on it? Thanks much. -Thomas -- *Thomas DeRamus* UAB Department of Psychology, Behavioral Neuroscience Graduate Research Trainee Civitan International Research Center 1719 6th Ave S, Suite 235J, Birmingham, AL 35233 *Phone*: 205-934-0971 *Email:* tpderamus(a)gmail.com, faustus(a)uab.edu

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mri_volcluster problems
by Joo Tan 21 Oct '14

21 Oct '14

Hi I have a few questions regarding the use of “mri_volcluster” for clusterwise thresholding. Essentially, we have a volume of pvalues obtained from a two tailed t-test comparing beta values from condition A vs condition B. When I ran this command: mri_volcluster --in transformedP.nii --sum cwthres_sum.txt --sign abs --fwhm 6 --mask avgmas.nii --cwpvalthresh 0.05 --thmin 2.3 --cwsig cwthresclusters.cwsig.nii I got this error message: ERROR: you must specify a pos or neg sign with --fwhm Input was –log10(p-value) Thmin was –log10(primary p threshold) Is there a way to perform clusterwise thresholding on results from two tailed tests? Is it essential to use –log10(p) values as input? Can I use raw t-values or any other values as input and thmin instead? Finally, are there any papers/references which this command is based on? Many thanks!

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unpacksdcmdir
by Alshikho, Mohamad J. 21 Oct '14

21 Oct '14

Hi , The command <unpacksdcmdir> is only used to unpack Siemens DICOM files, How can I unpack the non Siemens DICOMs? Thanks, Moahmad

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Do not have 'recon edit' button! Please help!
by Smaragdi A. 21 Oct '14

21 Oct '14

Dear mailing list, I am really stuck here.. Please can someone tell me why I cannot find the recon edit button on freeview! I am not sure if the version of Freesurfer I have downloaded is old, or I have done something wrong, or I am just blind.. But I cannot find the 'recon edit' button in freeview (see attached screenshot). What am I not doing right? Many thanks, Areti

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skewed CT viewed in tkregister2
by Borzello, Mia 21 Oct '14

21 Oct '14

Hi Freesurfers I'm having trouble viewing a CT in tkregister2. Here is the output from bugr: FREESURFER_HOME: /usr/local/freesurfer/stable5_3_0 Build stamp: freesurfer-Linux-centos6_x86_64-stable-v5.3.0-20130514 RedHat release: CentOS release 6.5 (Final) Kernel info: Linux 2.6.32-431.20.3.el6.x86_64 x86_64 NMR Center info (/space/freesurfer exists): machine: huygens SUBJECTS_DIR: /autofs/space/huygens_001/users/mia/subjects PWD: /autofs/space/huygens_001/users/mia/subjects/BI20v2_SurferOutput/mri ssh huygens setenv SUBJECTS_DIR /autofs/space/huygens_001/users/mia/subjects cd /autofs/space/huygens_001/users/mia/subjects/BI20v2_SurferOutput/mri 1. BI20 2. tkregister2 --s BI20v2_SurferOutput --mov ct.mgz --reg spm.reg.dat --surf orig 3. the coronal/saggital/horizontal views were skewed 4.attached. Thanks a lot, Mia

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T1 white matter segmentation
by Alshikho, Mohamad J. 21 Oct '14

21 Oct '14

Hi FS Experts, The T1 and T2 images in my data set are as the attached images. Kindly do you recommend that I do segmentation for the white matter using recon-all on my T1 or T2 images or to the output of a registration process between T1 and T2 I highly appreciate any recommendation Looking forward to learn from you and thank you Mohamad

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GLM contrast vector for continuous variable interaction
by Bronwyn Overs 20 Oct '14

20 Oct '14

Dear Freesurfer Mailing List, I am running a GLM in freesurfer 2 categorical variables (2 levels), and three continuous variables (age, prs, & sles). My fsgd file looks like this: GroupDescriptorFile 1 Title GLM_sles-group-gender-age Class genderMale-groupControl Class genderFemale-groupControl Class genderMale-groupAtRisk Class genderFemale-groupAtRisk Variables age prs sles Input ID_001 genderFemale-groupAtRisk 20 21 96 Input ID_002 genderFemale-groupControl 21 13 79 ... I want to test for an interaction between prs and sles, while controlling for age, gender, and group. Can you tell me if the following contrast vector will correctly test for this interaction: 0 0 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0 0 0 0 -- Kind regards, Bronwyn Overs Research Assistant Neuroscience Research Australia Neuroscience Research Australia Margarete Ainsworth Building Barker Street Randwick Sydney NSW 2031 Australia *M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265 neura.edu.au <http://neura.edu.au> Follow @neuraustralia on twitter <https://twitter.com/neuraustralia>Follow NeuRA on facebook <https://www.facebook.com/NeuroscienceResearchAustralia>Subscribe to the NeuRA Magazine <http://www.neura.edu.au/help-research/subscribe>

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